update ch_pp-ocrv3_det (#2032)

* update ch_pp-ocrv3_det

* Add det_db_score_mode (#1)
Co-authored-by: littletomatodonkey <dazhiningsibuqu@163.com>

* fix a unused param

* update defalut value

* update README

* fix a typo
Co-authored-by: littletomatodonkey <dazhiningsibuqu@163.com>

modules/image/text_recognition/ch_pp-ocrv3_det/README.md

@@ -84,26 +84,29 @@
 
 
   - ```python
-    def detect_text(paths=[],
-                    images=[],
+    def detect_text(images=[],
+                    paths=[],
                     use_gpu=False,
                     output_dir='detection_result',
-                    box_thresh=0.5,
                     visualization=False,
-                    det_db_unclip_ratio=1.5)
+                    box_thresh=0.6,
+                    det_db_unclip_ratio=1.5,
+                    det_db_score_mode="fast")
     ```
 
     - 预测API，检测输入图片中的所有中文文本的位置。
 
     - **参数**
 
-      - paths (list\[str\]): 图片的路径；
       - images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式；
+      - paths (list\[str\]): 图片的路径；
       - use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA_VISIBLE_DEVICES环境变量**
       - box\_thresh (float): 检测文本框置信度的阈值；
       - visualization (bool): 是否将识别结果保存为图片文件；
       - output\_dir (str): 图片的保存路径，默认设为 detection\_result；
       - det\_db\_unclip\_ratio: 设置检测框的大小；
+      - det\_db\_score\_mode: 设置检测得分计算方式，“fast” / “slow”
+
     - **返回**
 
       - res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为：
@@ -158,6 +161,10 @@
 
   初始发布
 
+* 1.1.0
+
+  移除 Fluid API
+
   - ```shell
-    $ hub install ch_pp-ocrv3_det==1.0.0
+    $ hub install ch_pp-ocrv3_det==1.1.0
     ```

modules/image/text_recognition/ch_pp-ocrv3_det/module.py

@@ -19,21 +19,15 @@ from __future__ import print_function
 import argparse
 import ast
 import base64
-import math
 import os
 import time
 
 import cv2
 import numpy as np
-import paddle.fluid as fluid
 import paddle.inference as paddle_infer
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import create_paddle_predictor
-from paddle.fluid.core import PaddleTensor
 from PIL import Image
 
-import paddlehub as hub
-from paddlehub.common.logger import logger
+from paddlehub.utils.utils import logger
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -48,15 +42,14 @@ def base64_to_cv2(b64str):
 
 @moduleinfo(
     name="ch_pp-ocrv3_det",
-    version="1.0.0",
+    version="1.1.0",
     summary=
     "The module aims to detect chinese text position in the image, which is based on differentiable_binarization algorithm.",
     author="paddle-dev",
     author_email="paddle-dev@baidu.com",
     type="cv/text_recognition")
-class ChPPOCRv3Det(hub.Module):
-
-    def _initialize(self, enable_mkldnn=False):
+class ChPPOCRv3Det:
+    def __init__(self, enable_mkldnn=False):
         """
         initialize with the necessary elements
         """
@@ -211,7 +204,7 @@ class ChPPOCRv3Det(hub.Module):
         postprocessor = DBPostProcess(
             params={
                 'thresh': 0.3,
-                'box_thresh': 0.6,
+                'box_thresh': box_thresh,
                 'max_candidates': 1000,
                 'unclip_ratio': det_db_unclip_ratio,
                 'det_db_score_mode': det_db_score_mode,
@@ -243,7 +236,7 @@ class ChPPOCRv3Det(hub.Module):
                 dt_boxes_list = postprocessor(outs_dict, [ratio_list])
                 dt_boxes = dt_boxes_list[0]
                 boxes = self.filter_tag_det_res(dt_boxes_list[0], original_image.shape)
-                res['data'] = boxes.astype(np.int).tolist()
+                res['data'] = boxes.astype(np.int64).tolist()
                 all_imgs.append(im)
                 all_ratios.append(ratio_list)
                 if visualization:
@@ -319,7 +312,7 @@ class ChPPOCRv3Det(hub.Module):
         self.arg_config_group.add_argument(
             '--det_db_score_mode',
             type=str,
-            default="str",
+            default="fast",
             help="method to calc the final det score, one of fast(using box) and slow(using poly).")
 
     def add_module_input_arg(self):

modules/image/text_recognition/ch_pp-ocrv3_det/processor.py

@@ -20,11 +20,8 @@ import sys
 
 import cv2
 import numpy as np
-import paddle
 import pyclipper
-from PIL import Image
 from PIL import ImageDraw
-from PIL import ImageFont
 from shapely.geometry import Polygon
 
 
@@ -168,9 +165,9 @@ class DBPostProcess(object):
 
             box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
             box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height)
-            boxes.append(box.astype(np.int16))
+            boxes.append(box.astype(np.int64))
             scores.append(score)
-        return np.array(boxes, dtype=np.int16), scores
+        return np.array(boxes, dtype=np.int64), scores
 
     def unclip(self, box):
         unclip_ratio = self.unclip_ratio
@@ -208,15 +205,15 @@ class DBPostProcess(object):
         '''
         h, w = bitmap.shape[:2]
         box = _box.copy()
-        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
-        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
-        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
-        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int64), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int64), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int64), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int64), 0, h - 1)
 
         mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
         box[:, 0] = box[:, 0] - xmin
         box[:, 1] = box[:, 1] - ymin
-        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int64), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
     def box_score_slow(self, bitmap, contour):
@@ -237,7 +234,7 @@ class DBPostProcess(object):
         contour[:, 0] = contour[:, 0] - xmin
         contour[:, 1] = contour[:, 1] - ymin
 
-        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int64), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
     def __call__(self, outs_dict, shape_list):

modules/image/text_recognition/ch_pp-ocrv3_det/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import paddlehub as hub
+
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://unsplash.com/photos/KTzZVDjUsXw/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzM3fHx0ZXh0fGVufDB8fHx8MTY2MzUxMTExMQ&force=true&w=640'
+        if not os.path.exists('tests'):
+            os.makedirs('tests')
+        response = requests.get(img_url)
+        assert response.status_code == 200, 'Network Error.'
+        with open('tests/test.jpg', 'wb') as f:
+            f.write(response.content)
+        cls.module = hub.Module(name="ch_pp-ocrv3_det")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('detection_result')
+
+    def test_detect_text1(self):
+        results = self.module.detect_text(
+            paths=['tests/test.jpg'],
+            use_gpu=False,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [[[261, 202], [376, 202], [376, 239], [
+                         261, 239]], [[283, 162], [352, 162], [352, 202], [283, 202]]])
+
+    def test_detect_text2(self):
+        results = self.module.detect_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [[[261, 202], [376, 202], [376, 239], [
+                         261, 239]], [[283, 162], [352, 162], [352, 202], [283, 202]]])
+
+    def test_detect_text3(self):
+        results = self.module.detect_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=True,
+            visualization=False,
+        )
+        self.assertEqual(results[0]['data'], [[[261, 202], [376, 202], [376, 239], [
+                         261, 239]], [[283, 162], [352, 162], [352, 202], [283, 202]]])
+
+    def test_detect_text4(self):
+        results = self.module.detect_text(
+            images=[cv2.imread('tests/test.jpg')],
+            use_gpu=False,
+            visualization=True,
+        )
+        self.assertEqual(results[0]['data'], [[[261, 202], [376, 202], [376, 239], [
+                         261, 239]], [[283, 162], [352, 162], [352, 202], [283, 202]]])
+
+    def test_detect_text5(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.detect_text,
+            images=['tests/test.jpg']
+        )
+
+    def test_detect_text6(self):
+        self.assertRaises(
+            AssertionError,
+            self.module.detect_text,
+            paths=['no.jpg']
+        )
+
+    def test_save_inference_model(self):
+        self.module.save_inference_model('./inference/model')
+
+        self.assertTrue(os.path.exists('./inference/model.pdmodel'))
+        self.assertTrue(os.path.exists('./inference/model.pdiparams'))
+
+
+if __name__ == "__main__":
+    unittest.main()
+